Can I train the SAME neural network with multiple datasets using Neural Fitting Tool?

After training the network for the first time I get the option of either retraining it with the same data set, or by pressing NEXT just to test it over a new one. If I go back and add a new data set it seems that the former network is lost and training begins from scratch.

[Aquatris]

You can combine all the data sets you want to use to train your NN into a single dataset simply by cascading them.

For instance, you have data set 1 with input u1 and output y1, and data set 2 with u2 and y2. than you create a new data set by simply defining u = [u1;u2] and y = [y1;y2].

[Georgios Etsias]

The training datasets should have the same dimension with the dataset I'll use for prediction though?

Answer 1

[Greg Heath]

In order to not forget a learned dataset while learning a new one:

 Continue training with a mixture (NOT a concatenation!) of the 
two. The training ratio should be approximately the same as the 
expected ratio in the implementation space.

Hope this helps.

Thank you for formally accepting my answer

Greg

[Ardiansyah Fauzi]

I have the same problem and I would like to mixture my training dataset. However, I am quite new in this field, do you have any recommendation method for this?

Thank you.

[Greg Heath]

See my answer before your comment.

Any specific questions?

Greg

[Emily LaPrime]

Greg,

Could you elaborate on creating a mixture of the two data sets? I am trying to train a network to classify using 4 sets of training data. I need the network to learn each set of data so that when I use the testing data the output is classified using the results of the data used to train.